White Paper: Particle Size Analysis for Process Optimization
This white paper introduces some of the most common particle size analysis approaches and how they can be deployed for the effective delivery of high quality particle products. Specifically, scientists are now combining offline particle size analyzers with in-process particle characterization instruments to optimize and improve processes. Examples are given where this combined approach enables scientists to:
- Obtain detailed process understanding by directly measuring changes to particle size and count as process parameters vary
- Determine operating conditions required to deliver fit-for-purpose particles on a consistent basis
- Monitor and correct process deviations during continuous or batch production
- Avoid time delays and errors associated with sampling, preparation and offline analysis
Common applications include crystallization, emulsification, suspensions, flocculation, dispersion, homogenization, wet milling, polymerization, microencapsulation, oil-water separation, disintegration and dissolution.
Particles: Problem or Opportunity?
Particles, crystals and droplets occur in many chemical processes, across a range of industries, and often pose challenges for scientists and engineers who are tasked with optimizing product quality and process efficiency. Characterizing particle properties effectively, in particular particle size and count, allows processing problems to be solved and product quality to be improved. Historically, scientists have relied on off-line particle size analyzers, such as laser diffraction or sieving, to perform this type of characterization. But in recent years, newer technologies have emerged that describe particle size and count in real time, as particles naturally exist in process. In process measurement of particles can reduce the error associated with offline sampling, and can provide continuous information about how particles behave under changing process conditions, allowing scientists to understand and optimize difficult processes using evidence-based methods. Crystallization in particular is a challenging particle formation process where the particle size after isolation can have a dramatic impact on all downstream processing operations.